Signal quality metric for cardiovascular time series

ABSTRACT

A method and system for determining a signal quality metric of a cardiovascular time series utilizing a wireless sensor device are disclosed. In a first aspect, the method comprises determining subsequent values of the cardiovascular time series and comparing the determined subsequent values to a threshold value. In a second aspect, a wireless sensor device comprises a processor and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to determine subsequent values of the cardiovascular time series and compare the determined subsequent values to a threshold value.

FIELD OF THE INVENTION

The present invention relates to wireless sensor devices, and moreparticularly, to determining a signal quality metric for cardiovasculartime series utilizing such wireless sensor devices.

BACKGROUND

Wireless sensor devices are used in a variety of applications includingcardiovascular health monitoring of patients. In many of theseapplications, a wireless sensor device is attached directly to theuser's skin (e.g. near the chest area) to measure certain data such ascardiovascular time series. Cardiovascular time series are certainphysiological signal features used to monitor physiological as well aspathological changes in a variety of patients.

Cardiovascular time series are extracted on a beat-to-beat basis from anelectrocardiogram (ECG) signal and on a pulse-to-pulse basis from pulseoximetric photoplethysmogram (PPG) or noninvasive blood pressurewaveforms. Motion and noise artifacts corrupt the raw signal waveformsand the derived cardiovascular time series as well. Conventional methodsto accurately detect/reduce artifacts suffer from limitations thatinclude not being optimal for in-band noise and prolonged artifactevents, failing to preserve absolute time, failing to ensure artifactfree in the derived time series, and not being applicable to varioustypes of motion artifacts. Therefore, there is a strong need for acost-effective and efficient solution that overcomes the aforementionedissues. The present invention addresses such a need.

SUMMARY OF THE INVENTION

A method and system for determining a signal quality metric of acardiovascular time series utilizing a wireless sensor device aredisclosed. In a first aspect, the method comprises determiningsubsequent values of the cardiovascular time series and comparing thedetermined subsequent values to a threshold value.

In a second aspect, a wireless sensor device comprises a processor and amemory device coupled to the processor, wherein the memory deviceincludes an application that, when executed by the processor, causes theprocessor to determine subsequent values of the cardiovascular timeseries and compare the determined subsequent values to a thresholdvalue.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures illustrate several embodiments of the inventionand, together with the description, serve to explain the principles ofthe invention. One of ordinary skill in the art readily recognizes thatthe embodiments illustrated in the figures are merely exemplary, and arenot intended to limit the scope of the present invention.

FIG. 1 illustrates a wireless sensor device in accordance with anembodiment.

FIG. 2 illustrates a method of determining a Time Series Signal Quality(TSQ) of a cardiovascular time series in accordance with a firstembodiment.

FIG. 3 illustrates a method of determining a Time Series Signal Quality(TSQ) of a cardiovascular time series in accordance with a secondembodiment.

DETAILED DESCRIPTION

The present invention relates to wireless sensor devices, and moreparticularly, to determining a signal quality metric for cardiovasculartime series. The following description is presented to enable one ofordinary skill in the art to make and use the invention and is providedin the context of a patent application and its requirements. Variousmodifications to the preferred embodiment and the generic principles andfeatures described herein will be readily apparent to those skilled inthe art. Thus, the present invention is not intended to be limited tothe embodiments shown but is to be accorded the widest scope consistentwith the principles and features described herein.

The accurate detection and reduction of motion and noise artifacts inphysiological signals detected by a wireless sensor device enables moreaccurate extraction of cardiovascular time series. In one embodiment,signal processing techniques and motion artifact detection algorithmsthat identify corrupted signal portions and exclude/delete the corruptedsignal portions in subsequent analysis are used to extract thecardiovascular time series. In another embodiment, the effects of motionartifacts are minimized by other signal processing techniques thatfilter or reconstruct raw waveforms of the physiological signals beforeextracting the cardiovascular time series.

The aforementioned signal processing techniques are effective for burstsof isolated artifact events that last only a short duration but are notoptimal for prolonged artifact events as well as in-band noiseartifacts. Additionally, excluding the corrupted signal portions failsto preserve absolute time in the subsequent analysis and signalreconstruction of motion artifact time periods using templates fails toreflect the true dynamics of the physiological signals. The motionartifact detection algorithms also often fail to detect certainphysiological artifacts such as ectopic beats.

Accordingly, a method and system in accordance with the presentinvention determines a signal quality metric that provides concurrentquality assessment and can be utilized for all types of cardiovasculartime series generated by a wireless sensor device. An algorithmicprocess is utilized by a wireless sensor device that has been attachedto a user to identify physical and physiological artifacts in thederived cardiovascular time series signals and to provide a concurrentTime Series Signal Quality (TSQ) metric.

Examples of cardiovascular time series derived from ECG signals includeRR intervals that provide instantaneous heart rate, QRS amplitude/areathat are used as a surrogate respiratory signal, and time intervals suchas PR, QRS, and ST. Examples of cardiovascular time series derived frompulse oximetry signals include peak-to-peak (PP) pulse intervals thatprovide instantaneous pulse rate, systolic pulse amplitude that monitorsphysiological changes related to oxygen saturation, respiration, andblood volume, and reflection index that measures the vascular stiffness.

Examples of cardiovascular time series derived from noninvasive bloodpressure signals include beat-to-beat systolic, diastolic, and meanblood pressure measurements. An example of cardiovascular time seriesderived from more than one signal include a pulse transmit time thatmeasures vascular stiffness and inspiratory effort and that is obtainedas a time difference between the R wave peak ECG to the onset orupstroke of the peripheral pulse waveform.

In one embodiment, the TSQ is derived by a wireless sensor device usinga first process algorithm that compares two subsequent beats or cardiaccycles (e.g. N and N+1) for the detection of artifacts with respect to aThreshold Value (TH) which is determined from the signal variability ofthe cardiovascular time series. In this embodiment, the entire timeseries data is taken into consideration making the first process moresuitable for an offline analysis.

In one embodiment, the TSQ is derived by a wireless sensor device usinga second process algorithm that compares three subsequent beats (e.g. N,N−1, N+1) for the detection of artifacts with respect to a ReferenceValue (Ref) and a Threshold Value (TH). In this embodiment, the Ref isupdated based on the time series values of a number of normalconsecutive beats or cardiac cycles (Nb) window (e.g. 5 beats) and theTH is a “priori” estimate of the time series variability. The secondprocess is more suitable for real-time or online analysis.

One of ordinary skill in the art readily recognizes that thecardiovascular time series utilized by the wireless sensor device usingthe first and second processes can be any of the aforementioned examplesincluding but not limited to RR intervals and QRS area and that would bewithin the spirit and scope of the present invention.

One of ordinary skill in the art readily recognizes that a variety ofwireless sensor devices can be utilized to measure the physiologicalsignals utilized to determine the cardiovascular time series and theassociated TSQ including but not limited to a wireless sensor device ina patch form-factor, tri-axial accelerometers, uni-axial accelerometers,bi-axial accelerometers, gyroscopes, and pressure sensors and that wouldbe within the spirit and scope of the present invention.

FIG. 1 illustrates a wireless sensor device 100 in accordance with anembodiment. The wireless sensor device 100 includes a sensor 102, aprocessor 104 coupled to the sensor 102, a memory 106 coupled to theprocessor 104, an application 108 coupled to the memory 106, and atransmitter 110 coupled to the application 108. In one embodiment, thewireless sensor device 100 is attached, in any orientation to a user andon any location of the user. In another embodiment, the wireless sensordevice 100 is chest-mounted to the user. The sensor 102 obtains datafrom the user and transmits the data to the memory 106 and in turn tothe application 108. The processor 104 executes the application 108 tomonitor physiological signals and derive the TSQ. The information istransmitted to the transmitter 110 and in turn relayed to another useror device.

In one embodiment, the sensor 102 is bipolar electrodes or amicroelectromechanical system (MEMS) tri-axial accelerometer and theprocessor 104 is a microprocessor. One of ordinary skill in the artreadily recognizes that a variety of devices can be utilized for thesensor 102, the processor 104, the memory 106, the application 108, andthe transmitter 110 and that would be within the spirit and scope of thepresent invention.

To describe the features of the present invention in more detail, refernow to the following description in conjunction with the accompanyingFigures.

FIG. 2 illustrates a method 200 of determining a Time Series SignalQuality (TSQ) of a cardiovascular time series in accordance with a firstembodiment. In FIG. 2, the wireless sensor device 100 is attached to auser to measure various cardiovascular signals (e.g. an ECG signal). Aninstantaneous time series (ITS) of length N (number of values) is fed asan input to a first process algorithm by the wireless sensor device 100,via step 202. The output of the first process algorithm, InstantaneousTime Series Signal Quality (ITSQ), is initialized by using a N×1 vectorwith N number of zeros to predefine an ITSQ value as corrupted denotedby 0 values per the equation ITSQ=zeros(N,1), where the N number of rowsare all 0 values, via step 204. An Element Number (EN) is defined asEN=[1, 2, 3 . . . N] by using a N×1 vector that assigns a relativeposition of the elements in the ITS, via step 206.

In FIG. 2, an N series array (TS_(N)) is generated by eliminating thelast element of the ITS and making a new vector where TS_(N)=[ITS(1),ITS(2) . . . ITS(N−1)], via step 208. An N+1 series array (TS_(N+1)) isgenerated by eliminating the first element of the ITS and making a newvector where TS_(N+1)=[ITS(2), ITS(3) . . . ITS(N)], via step 210.Element-wise division (ED) is obtained between the two arrays TS_(N) andTS_(N+1) per the equations ED_(N)=TS_(N)•/TS_(N+1) andED_(N+1)=TS_(N+1)•/TS_(N), via step 212.

In FIG. 2, indices (IND1) are found that satisfy any of the equationsED_(N)<(1−TH), ED_(N)>(1+TH), ED_(N+1)<(1−TH), or ED_(N+1)>(1+TH) whereTH equals a threshold for artifact detection that is either predefinedby the user or determined from the cardiovascular signal, via step 214.The indices IND1 are sorted and added with 1 (e.g., IND1=IND1+1) tocompensate for the division between the N and N+1 series arrays, viastep 216. The element numbers EN corresponding to the indices IND1 arethe potential artifact candidates. Hence, the IND1 elements of the ITSand element number EN series that offer the relative position of timeseries values of the ITS are removed per the equation ITS(IND1)=[ ];EN(IND1)=[ ], via step 218.

Steps 208 to 218 are repeated to obtain indices (IND2), via step 220.The potential artifact candidates given by the indices IND2 are removedfrom the ITS and EN series per the equation ITS(IND2)=[ ]; EN(IND2)=[ ],via step 222. The elements of ITS that remain represent a cleanerinstantaneous time series with reduced artifacts. The remaining elementsof EN represent relative positions of the original input time seriesthat correspond to clean data.

In FIG. 2, the remaining EN elements of the Instantaneous Time SeriesSignal Quality (ITSQ) array are set to 1 per the equation ITSQ(EN)=1 sothat the ITSQ array includes both 0 and 1 values that correspond tocorrupted (0 values) and clean (1 values) data, respectively, via step224. The modified ITSQ array from step 224 represents one form of analgorithmic output of the method 200. The ITSQ array is initiallypredefined with zeros of length N×1, per step 204, and then the ENindices of ITSQ are changed to 1 values, per step 224, so the modifiedITSQ array includes both 0 and 1 values indicating corrupt and cleanvalues, respectively.

The time course of the modified ITSQ array from step 224 is the same asthe ITS and is either a beat number or an instantaneous time denotingthe beat or cardiac cycle. If the ITS is generated with respect to beatnumber, the modified ITSQ array will also be based on beat number. Ifthe time course of the ITS is given with respect to instantaneous timesequence, the modified ITSQ array will also be non-uniformly spacedaccording to instantaneous time samples that are determined by theincidence of non-uniform cardiac cycles.

In order to obtain a Uniformly-Sampled Signal Quality (UTSQ) as anotherform of the algorithmic output of the method 200 at a user-defineduniform sampling rate of Fs Hertz (Hz) (e.g. 4 Hz), interpolationtechniques are performed between the ITSQ values of the ITSQ array andthe respective instantaneous time sequence for the ITS, via step 226. Inone embodiment, nearest neighbor interpolation is utilized which offersbinary values (either 0 or 1) as a quality estimate and in anotherembodiment, cubic interpolation is utilized which offers values between0 and 1 as a quality estimate.

FIG. 3 illustrates a method 300 of determining a Time Series SignalQuality (TSQ) of a cardiovascular time series in accordance with asecond embodiment. In FIG. 3, the wireless sensor device 100 is attachedto a user to measure various cardiovascular signals (e.g. an ECG signal)and generate instantaneous time series (ITS). In FIG. 3, theabbreviation TS is a temporary array as a copy of the instantaneous timeseries (ITS); Nb is the number of normal consecutive beats; Count1 is acounter that tracks the normal values with reference to Nb; Count2 is acounter that tracks artifact values with reference to Nb; Ref is areference value that changes continuously; i is a sample number; TS_(Th)is an artifact threshold determined from signal variability; and Fs is aresampling frequency (Hz).

The instantaneous time series (ITS) of length N (number of values) isdetected by the wireless sensor device 100, via step 302. The ITS isduplicated to generate TS by making a copy of the instantaneous timeseries and saving the copy as a new vector, via step 304. Two countersCount1 and Count2 are set to 0 and an initial reference value (Ref) iscalculated as a mean of the entire time series ITS, via step 306. In oneembodiment, the Nb value is set to 5 beats.

In FIG. 3, a second process algorithm loops through values of the ITSone-by-one and determines each value as either clean or corrupted dataand subsequently generates an ITSQ series with 1 and 0 values torepresent clean and corrupt values, respectively. The second processalgorithm first considers the second sample of the ITS as current valuesample (e.g., i=2), via step 308. The reference value is updated per thesatisfaction of four conditions where i refers to a current valuesample, i−1 refers to a previous value, and i+1 refers to a next value,via block 310.

The four conditions of block 310 are represented per the equations: a)TS(i)/TS(i−1)>(1−TS_(Th)), b) TS(i)/TS(i−1)<(1+TS_(Th)), c)TS(i)/Ref>(1−TS_(Th)), and d) TS(i)/Ref<(1+TS_(Th)). The four conditionsof block 310 compare the present and previous values of the time seriescopy TS with respect to the boundaries of the normal threshold(1−TS_(Th) and 1+TS_(Th)), where TS_(Th) is an artifact threshold valuethat can be predefined by the user or determined from the cardiovascularsignal.

In FIG. 3, if all four conditions are satisfied via block 310, counter 1is incremented per the equation Count1=Count1+1, via step 312, and thenif Count1 satisfies the equation Count1>=Nb, via step 314, the referencevalue (Ref) is updated according to the current time series value TS(i)per the equation Ref=TS(i), via step 316. If any of the four conditionsof block 310 are not satisfied, a second set of four conditions arecompared that involve the present and the next values of the time seriescopy TS and the previous value of original time series ITS to identifypotential motion artifacts followed by a previously declared artifactevent, via block 322.

The four conditions of block 322 are represented per the equations: a)TS(i)/ITS(i−1)>(1−TS_(Th)), b) TS(i)/ITS(i−1)<(1+TS_(Th)), c)TS(i+1)/TS(i)>(1−TS_(Th)), and d) TS(i+1)/TS(i)<(1+TS_(Th)). In thefirst two conditions, the previous values (i−1) are taken from theoriginal time series ITS instead of the time series copy TS because theprevious value of TS could be “0” if it had been declared as corruptduring the last loop.

Block 322 identifies an episode of certain time series values followedby an artifact event that may be within the artifact thresholdboundaries when 3 successive values are compared and thus may resemblenormal values but could be “corrupted” artifact values. If the length ofthe consecutive time series values that satisfy the four conditions ofblock 322 is greater than Nb (the number of normal consecutive beats),then the identified time series values are denoted as normal values,otherwise the identified time series values are determined to beartifact values.

In FIG. 3, if all four conditions are satisfied via block 322, counter 2is incremented per the equation Count2=Count2+1, via step 324, and thenif Count2 satisfies the equation Count2>=Nb, via step 326, the referencevalue (Ref) is updated as the current time series value TS(i), via step316. If Count2 is not greater than or equal to Nb, then TS(i) isdetermined to be an artifact and set to 0, via step 328, and Count1 isreset to 0, via step 330. If any of the four conditions of block 322 arenot satisfied, a third set of four conditions are compared, via block332.

In FIG. 3, the four conditions of block 332 are represented per theequations: a) TS(i)/Ref<(1−TS_(Th)), b) TS(i)/Ref>(1+TS_(Th)), c)TS(i+1)/Ref<(1−TS_(Th)), and d) TS(i+1)/Ref>(1+TS_(Th)). The fourconditions of block 332 involve comparing the relationships betweenpresent (i) or next (i+1) time series values to the reference value Ref.If any of the four conditions of block 332 are satisfied, the currentvalue sample (i) is confirmed to be an artifact and the current value ofthe time series copy TS is changed to 0 per the equation TS(i)=0, viastep 334, and both counters are reset as Count1=0 and Count2=0, via step336.

After any of steps 316, 330, 336 and when Count1 is not determined to begreater than or equal to Nb per step 314, the current sample i iscompared to the length of TS minus 1 per the equation i<length(TS)−1,via step 318. If the current value sample (i) does not satisfy thecondition in step 318 (e.g. i is not less than the length(TS)−1), thenthe current value sample is a penultimate value of the whole time seriesand a looping process of the method 300 for artifact detection iscomplete. Therefore, the TSQ is formulated based on zero elementsgenerated in the temporary array TS during stages of artifact detection,via block 338. If i is less than length(TS)−1, then i is incremented,via step 320, and the looping process of the method 300 returns back toblock 310 to start the calculations again.

For the formulation of ITSQ, the ITSQ time series is first predefinedwith all 1 values of length ITS per the equationITSQ=ones(length(ITS),1) and then indices of a modified time series TSwith 0 values are found and the indices values of the ITSQ time seriesare set to 0 per the equation ITSQ(TS==0)=0, via block 338. The modifiedITSQ array from block 338 includes both 0 and 1 values that correspondto corrupted (0 values) and clean (1 values) data, respectively. Themodified ITSQ array from block 338 represents one form of an algorithmicoutput of the method 300.

The time course of the ITSQ array from block 338 is the same as the ITSand is either a beat number or an instantaneous time denoting the beator cardiac cycle. If the ITS is generated with respect to beat number,the ITSQ array will also be based on beat number. If the time course ofthe ITS is given with respect to instantaneous time sequence, the ITSQarray will also be non-uniformly spaced according to instantaneous timesamples that are determined by the incidence of non-uniform cardiaccycles.

Once again, in order to obtain a Uniformly-Sampled Signal Quality (UTSQ)as another form of the algorithmic output of the method 300 at auser-defined uniform sampling rate of Fs Hertz (Hz) (e.g. 4 Hz),interpolation techniques are performed between the ITSQ values of theITSQ array and the respective instantaneous time sequence for the ITS,via step 340. In one embodiment, nearest neighbor interpolation isutilized, via step 340 a, which offers binary values (either 0 or 1) asa quality estimate and in another embodiment, cubic interpolation isutilized, via step 340 b, which offers values between 0 and 1 as aquality estimate.

In one embodiment, the artifact threshold value (TH) is predefined as20% using typical RR and PP intervals thereby resulting in a variabilityindependent TH value. In another embodiment, cardiovascular time seriesmetrics such as R-wave amplitude (RWA) are utilized to determine TH perthe equation TH=(2*SD)/(Percentile Difference Between 95% and 5%)*100where SD refers to the standard deviation and the lower and upper limitsof TH are set to be 25% and 75% respectively thereby resulting in avariability dependent TH value.

If an input time series is completely clean and considered as Gaussian,the percentile difference between 95% and 5% measures dispersion of aprobability distribution of the input time series which will beapproximately 4*SD. For such an ideal input time series, the artifactthreshold (TH) is calculated per the equation TH≅(2*SD)/((4*SD)*100≅50%.The practical time series inputs may not be completely clean and mayhave artifacts. Thus, a probability distribution of the practical timeseries inputs is not a perfect Gaussian and varies based on the presenceof artifacts in the time series which affects the computation of TH. Theselection of upper and lower limits of TH between 25% and 75% isarbitrary. Thus, in this embodiment, as the artifact threshold value isderived based on signal variability, artifact detection is customizablefor individual recording and user personalization.

In one embodiment, a method determines a signal quality metric of acardiovascular time series utilizing a wireless sensor device bydetermining subsequent values of the cardiovascular time series and bycomparing the determined subsequent values to a threshold value. Thedetermination of subsequent values performs array-based calculations onthe subsequent values of the cardiovascular time series. The comparingstep determines whether each individual time series value of thecardiovascular time series is a normal “clean” value or an artifact“corrupt” value. In this embodiment, the threshold value is determinedfrom signal variability of the cardiovascular time series and thesubsequent values are determined from any of subsequent beats andcardiac cycles of the cardiovascular time series.

In another embodiment, the method includes comparing the determinedsubsequent time periods to a reference value in addition to thethreshold value using a decision tree model to determine whether eachindividual time series value of the cardiovascular time series is anormal “clean” value or an artifact “corrupt” value. The reference valueis continuously updated within a predetermined window represented by anumber of normal consecutive beats (Nb) using a first set of conditions.

In this embodiment, the method includes flagging at least one individualtime series value of the cardiovascular time series if a second set ofconditions is satisfied and identifying potential artifacts in thecardiovascular time series by comparing the at least one flaggedindividual time series value to the Nb. In one embodiment, if the atleast one flagged individual time series value is greater than Nb, thenthe at least one flagged individual time series value is determined tobe a normal value, but if the at least one flagged individual timeseries value is less than Nb, then the at least one flagged individualtime series value is determined to be a potential artifact value.

In this embodiment, the potential artifact value is confirmed as anartifact value if any of a third set of conditions is satisfied. In oneembodiment, the method includes updating the cardiovascular time serieswith 0 values for confirmed artifact values and with 1 values forconfirmed normal values. In one embodiment, the method includesformulating, by the wireless sensor device, the signal quality metricutilizing the updated cardiovascular time series and uniformly samplingthe signal quality metric using any of nearest neighbor and cubicinterpolation.

As above described, the method and system allow for determining a signalquality metric for all types of cardiovascular time series that havebeen derived by a wireless sensor device using detected physiologicalsignals (e.g. ECG signal). The signal quality metric is determined bythe wireless sensor device for the cardiovascular time series at a beatlevel as well as at a uniform sampling rate of Fs Hz (e.g. 1 to 4 Hz).The algorithmic processes utilized by the wireless sensor device per themethods 200 and 300 identify both physical and physiological artifactsin secondary signal levels that assist in correct interpretation infurther data analysis. The decisions regarding clean or corrupt valuesgenerated from both algorithmic processes are used to generate aconcurrent time series signal quality metric that indicates the qualityof the cardiovascular time series.

In one embodiment, by detecting a cardiovascular signal and deriving anassociated cardiovascular time series from the detected cardiovascularsignal, the wireless sensor device calculates a Time Series SignalQuality (TSQ) metric by analyzing subsequent beats or cardiac cycles andcomparing data values of the analyzed beats/cycles to a threshold valuethat is determined from the normal variability of the cardiovasculartime series utilizing a first algorithmic process (method 200). Inanother embodiment, the wireless sensor device calculates a Time SeriesSignal Quality (TSQ) from the associated cardiovascular time series bycomparing data values of the analyzed beats/cycles to both a referencevalue and the threshold value utilizing a second algorithmic process.

A method and system for determining a signal quality metric of acardiovascular time series that is detected by a wireless sensor devicehave been disclosed. Embodiments described herein can take the form ofan entirely hardware implementation, an entirely softwareimplementation, or an implementation containing both hardware andsoftware elements. Embodiments may be implemented in software, whichincludes, but is not limited to, application software, firmware,resident software, microcode, etc.

The steps described herein may be implemented using any suitablecontroller or processor, and software application, which may be storedon any suitable storage location or calculator-readable medium. Thesoftware application provides instructions that enable the processor toperform the functions described herein.

Furthermore, embodiments may take the form of a calculator programproduct accessible from a calculator-usable or calculator-readablemedium providing program code for use by or in connection with acalculator or any instruction execution system. For the purposes of thisdescription, a calculator-usable or calculator-readable medium can beany apparatus that can contain, store, communicate, propagate, ortransport the program for use by or in connection with the instructionexecution system, apparatus, or device.

The medium may be an electronic, magnetic, optical, electromagnetic,infrared, semiconductor system (or apparatus or device), or apropagation medium. Examples of a calculator-readable medium include asemiconductor or solid state memory, magnetic tape, a removablecalculator diskette, a random access memory (RAM), a read-only memory(ROM), a rigid magnetic disk, and an optical disk. Current examples ofoptical disks include DVD, compact disk-read-only memory (CD-ROM), andcompact disk—read/write (CD-RAN).

Although the present invention has been described in accordance with theembodiments shown, one of ordinary skill in the art will readilyrecognize that there could be variations to the embodiments and thosevariations would be within the spirit and scope of the presentinvention. Accordingly, many modifications may be made by one ofordinary skill in the art without departing from the spirit and scope ofthe appended claims.

What is claimed is:
 1. A method for determining a signal quality metricof a cardiovascular time series utilizing a wireless sensor device, themethod comprising: generating subsequent values of the cardiovasculartime series by performing array-based calculations on the cardiovasculartime series; comparing the generated subsequent values to a thresholdvalue and determining whether each individual time series value of thecardiovascular time series is a normal value or an artifact value byperforming element-wise division between the cardiovascular time seriesand the subsequent values of the cardiovascular time series to producethe signal quality metric comprising an array of instantaneoustime-series signal quality values; performing interpolation on thesignal quality metric to determine a uniformly sampled signal qualitymetric; and relaying, using a transmitter, the uniformly sampled signalquality metric to another user or device.
 2. The method of claim 1,wherein the threshold value is determined from signal variability of thecardiovascular time series.
 3. The method of claim 1, furthercomprising: comparing the generated subsequent values to a referencevalue in addition to the threshold value using a decision tree model todetermine whether each individual time series value of thecardiovascular time series is a normal value or an artifact value. 4.The method of claim 3, wherein the reference value is continuouslyupdated within a predetermined window represented by a number of normalconsecutive beats (Nb) using a first set of conditions.
 5. The method ofclaim 4, further comprising: flagging at least one individual timeseries value of the cardiovascular time series if a second set ofconditions is satisfied; and identifying potential artifacts in thecardiovascular time series by comparing the at least one flaggedindividual time series value to the Nb.
 6. The method of claim 5,wherein if the at least one flagged individual time series value isgreater than Nb, then the at least one flagged individual time seriesvalue is determined to be a normal value, but if the at least oneflagged individual time series value is less than Nb, then the at leastone flagged individual time series value is determined to be a potentialartifact value.
 7. The method of claim 6, wherein the potential artifactvalue is confirmed as an artifact value if any of a third set ofconditions is satisfied.
 8. The method of claim 7, further comprising:updating the cardiovascular time series with 0 values for confirmedartifact values and with 1 values for confirmed normal values.
 9. Themethod of claim 8, further comprising: formulating the signal qualitymetric utilizing the updated cardiovascular time series; and uniformlysampling the signal quality metric using any of nearest neighbor andcubic interpolation.
 10. A wireless sensor device for determining asignal quality metric of a cardiovascular time series, comprising: aprocessor; and a memory device coupled to the processor, wherein thememory device includes an application that, when executed by theprocessor, causes the processor to: generate subsequent values of thecardiovascular time series by performing array-based calculations on thecardiovascular time series; compare the generated subsequent values to athreshold value and determine whether each individual time series valueof the cardiovascular time series is a normal value or an artifact valueby performing element-wise division between the cardiovascular timeseries and the generated subsequent values of the cardiovascular timeseries to produce the signal quality metric comprising an array ofinstantaneous time-series signal quality values; perform interpolationon the signal quality metric to determine a uniformly sampled signalquality metric; and a transmitter to relay the uniformly sampled signalquality metric to another user or device.
 11. The system of claim 10,wherein the application, when executed by the processor, further causesthe processor to: compare the generated subsequent values to a referencevalue in addition to the threshold value using a decision tree model todetermine whether each individual time series value of thecardiovascular time series is a normal value or an artifact value. 12.The system of claim 11, wherein the reference value is continuouslyupdated within a predetermined window represented by a number of normalconsecutive beats (Nb) using a first set of conditions.
 13. The systemof claim 12, wherein the application, when executed by the processor,further causes the processor to: flag at least one individual timeseries value of the cardiovascular time series if a second set ofconditions is satisfied; and identify potential artifacts in thecardiovascular time series by comparing the at least one flaggedindividual time series value to the Nb.
 14. The system of claim 13,wherein if the at least one flagged individual time series value isgreater than Nb, then the at least one flagged individual time seriesvalue is determined to be a normal value, but if the at least oneflagged individual time series value is less than Nb, then the at leastone flagged individual time series value is determined to be a potentialartifact value.
 15. The system of claim 14, wherein the potentialartifact value is confirmed as an artifact value if any of a third setof conditions is satisfied.
 16. The system of claim 15, wherein theapplication, when executed by the processor, further causes theprocessor to: update the cardiovascular time series with 0 values forconfirmed artifact values and with 1 values for confirmed normal values.17. The system of claim 16, wherein the application, when executed bythe processor, further causes the processor to: formulate the signalquality metric utilizing the updated cardiovascular time series; anduniformly sample the signal quality metric using any of nearest neighborand cubic interpolation.